71 citations · 172 across the 38 of their papers we have counts for
42 papers
Dual-Mode Low-Rank Learner with Bridge-Prototype Ensemble for Vision-Language Class-Incremental Learning
Chiyuan He, Zihuan Qiu, Fanman Meng +5
Benefiting from transferable visual-textual alignment, CLIP has been widely adopted for class-incremental learning (CIL). However, existing learners either repeatedly update compon…
CLAP: Closed-Loop Alignment with Pressure for Precise Suction Manipulation
Yixian Zou, Chongyang Xu, Yuling Xin +3
Stacking and palletising demand precise placement: error left in one layer is inherited by the next, and a flat pad offers no feature to funnel a wrong pose into the right one. Top…
SAFE-Merge: Data-Free Continual Model Merging with General Knowledge Preservation
Zihuan Qiu, Zhiyang Liao, Chiyuan He +5
Data-free continual model merging must incorporate a stream of specialized models while retaining both pretrained general knowledge and previously acquired tasks, without access to…
FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking
Hai Jiang, Yixian Zou, Binbin Liang +3
Real-time deployment of Vision-Language-Action (VLA) policies necessitates asynchronous execution, wherein subsequent action chunks are computed concurrently with the execution of…
Test-time Ego-Exo-centric Adaptation for Action Anticipation via Multi-Label Prototype Growing and Dual-Clue Consistency
Zhaofeng Shi, Heqian Qiu, Lanxiao Wang +4
Efficient adaptation between Egocentric (Ego) and Exocentric (Exo) views is crucial for applications such as human-robot cooperation. However, the success of most existing Ego-Exo…
SAVA-X: Ego-to-Exo Imitation Error Detection via Scene-Adaptive View Alignment and Bidirectional Cross View Fusion
Xiang Li, Heqian Qiu, Lanxiao Wang +4
Error detection is crucial in industrial training, healthcare, and assembly quality control. Most existing work assumes a single-view setting and cannot handle the practical case w…
Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation
Chongyang Xu, Yixian Zou, Tianyu Yang +4
Diffusion-based visuomotor policies model complex action distributions through iterative denoising, but repeated inference adds latency to robotic control. One-step generators redu…
Continual Learning with Vision-Language Models via Semantic-Geometry Preservation
Chiyuan He, Zihuan Qiu, Fanman Meng +4
Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without explicitly preserving the cros…
Closing the Oracle Gap: Increment Vector Transformation for Class Incremental Learning
Zihuan Qiu, Yi Xu, Fanman Meng +4
Class Incremental Learning (CIL) aims to sequentially acquire knowledge of new classes without forgetting previously learned ones. Despite recent progress, current CIL methods stil…
Null-Space Filtering for Data-Free Continual Model Merging: Preserving Stability, Promoting Plasticity
Zihuan Qiu, Lei Wang, Yang Cao +7
Data-free continual model merging (DFCMM) aims to fuse independently fine-tuned models into a single backbone that evolves with incoming tasks without accessing task data. This pap…
DFR: A Decompose-Fuse-Reconstruct Framework for Multi-Modal Few-Shot Segmentation
Shuai Chen, Fanman Meng, Xiwei Zhang +4
This paper presents DFR (Decompose, Fuse and Reconstruct), a novel framework that addresses the fundamental challenge of effectively utilizing multi-modal guidance in few-shot segm…
SS-DC: Spatial-Spectral Decoupling and Coupling Across Visible-Infrared Gap for Domain Adaptive Object Detection
Xiwei Zhang, Chunjin Yang, Yiming Xiao +2
Unsupervised domain adaptive object detection (UDAOD) from the visible domain to the infrared (RGB-IR) domain is challenging. Existing methods regard the RGB domain as a unified do…
Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection
Taijin Zhao, Heqian Qiu, Yu Dai +4
Few-shot object detection (FSOD) aims to detect objects with limited samples for novel classes, while relying on abundant data for base classes. Existing FSOD approaches, predomina…
Leveraging Pre-Trained Models for Multimodal Class-Incremental Learning under Adaptive Fusion
Yukun Chen, Zihuan Qiu, Fanman Meng +3
Unlike traditional Multimodal Class-Incremental Learning (MCIL) methods that focus only on vision and text, this paper explores MCIL across vision, audio and text modalities, addre…
Your Demands Deserve More Bits: Referring Semantic Image Compression at Ultra-low Bitrate
Chenhao Wu, Qingbo Wu, Haoran Wei +5
With the help of powerful generative models, Semantic Image Compression (SIC) has achieved impressive performance at ultra-low bitrate. However, due to coarse-grained visual-semant…
MINGLE: Mixture of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging
Zihuan Qiu, Yi Xu, Chiyuan He +4
Continual model merging integrates independently fine-tuned models sequentially without access to the original training data, offering a scalable and efficient solution for continu…
CMaP-SAM: Contraction Mapping Prior for SAM-driven Few-shot Segmentation
Shuai Chen, Fanman Meng, Liming Lei +5
Few-shot segmentation (FSS) aims to segment new classes using few annotated images. While recent FSS methods have shown considerable improvements by leveraging Segment Anything Mod…
Unsupervised Ego- and Exo-centric Dense Procedural Activity Captioning via Gaze Consensus Adaptation
Zhaofeng Shi, Heqian Qiu, Lanxiao Wang +3
Even from an early age, humans naturally adapt between exocentric (Exo) and egocentric (Ego) perspectives to understand daily procedural activities. Inspired by this cognitive abil…
DesCLIP: Robust Continual Learning via General Attribute Descriptions for VLM-Based Visual Recognition
Chiyuan He, Zihuan Qiu, Fanman Meng +3
Continual learning of vision-language models (VLMs) focuses on leveraging cross-modal pretrained knowledge to incrementally adapt to expanding downstream tasks and datasets, while…
ARIC: An Activity Recognition Dataset in Classroom Surveillance Images
Linfeng Xu, Fanman Meng, Qingbo Wu +16
The application of activity recognition in the ``AI + Education" field is gaining increasing attention. However, current work mainly focuses on the recognition of activities in man…
Cognition Transferring and Decoupling for Text-supervised Egocentric Semantic Segmentation
Zhaofeng Shi, Heqian Qiu, Lanxiao Wang +3
In this paper, we explore a novel Text-supervised Egocentic Semantic Segmentation (TESS) task that aims to assign pixel-level categories to egocentric images weakly supervised by t…
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images
Yilei Qian, Kanglei Geng, Kailong Chen +5
The application of activity recognition in the "AI + Education" field is gaining increasing attention. However, current work mainly focuses on the recognition of activities in manu…
Distribution-Level Memory Recall for Continual Learning: Preserving Knowledge and Avoiding Confusion
Shaoxu Cheng, Kanglei Geng, Chiyuan He +7
Continual Learning (CL) aims to enable Deep Neural Networks (DNNs) to learn new data without forgetting previously learned knowledge. The key to achieving this goal is to avoid con…
No Re-Train, More Gain: Upgrading Backbones with Diffusion model for Pixel-Wise and Weakly-Supervised Few-Shot Segmentation
Shuai Chen, Fanman Meng, Chenhao Wu +5
Few-Shot Segmentation (FSS) aims to segment novel classes using only a few annotated images. Despite considerable progress under pixel-wise support annotation, current FSS methods…